Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

705
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
705
Association Areas of the Cortex01:21

Association Areas of the Cortex

9.8K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
9.8K
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.7K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.7K
Modeling and Similitude01:12

Modeling and Similitude

678
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
678
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

5.2K
The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
5.2K
Prosopagnosia01:24

Prosopagnosia

984
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
984

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2026
Same author

Systematic partisan content skews in TikTok during the 2024 US elections.

Nature·2026
Same author

A Scoping Review of Machine Learning Applications for Diagnosis, Classification, and Prognosis in Lupus Nephritis.

Kidney360·2026
Same author

Explainable Feature Embeddings from Histopathology Foundation Models: A Case Study for End Stage Kidney Disease Risk Analysis in Diabetic Nephropathy Patients.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Correction: The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south.

Frontiers in digital health·2026
Same author

On demographic transformation: why we need to think beyond silos.

Frontiers in aging·2026

関連する実験動画

Updated: Feb 25, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.6K

FaceScanPaliGemmaは,顔の特徴の認識のためのマルチエージェントビジョン言語モデルです.

Nouar AlDahoul1, Myles Joshua Toledo Tan2, Harishwar Reddy Kasireddy2

  • 1Computer Science Department, New York University Abu Dhabi, Abu Dhabi, UAE.

Scientific reports
|February 23, 2026
PubMed
まとめ

FaceScanPaliGemmaは,新しいマルチエージェントビジョン言語モデル (VLM) で,人種,性別,年齢,感情などの顔の属性を高精度で分類しています. このシステムは,ゼロショット評価において既存のモデルを上回る.

キーワード:
フェイススキャン パリ ゲーマ顔の認識機能 顔の認識機能マルチエージェントビジョン言語モデル ビジョン言語モデル

さらに関連する動画

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.9K

関連する実験動画

Last Updated: Feb 25, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.6K
Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.9K

科学分野:

  • コンピュータビジョン コンピュータビジョン
  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.

背景:

  • 顔の特徴認識技術にはさまざまな応用があるが,複雑さと表現の多様性により課題に直面している.
  • 顔の属性を分類するための既存の方法は,より高い精度が必要であることを示しています.

研究 の 目的:

  • FaceScanPaliGemmaを提案する. 強化された顔の属性分類のためのマルチエージェントビジョン言語モデル (VLM) システム.
  • 提案されたシステムの性能を,他の最先端のVLMと比較して評価する.

主な方法:

  • 4つの微調整されたGoogle PaliGemmaモデルで構成するシステムであるFaceScanPaliGemmaを開発し,それぞれが異なる顔の属性に特化しています.
  • システムの分類能力の包括的な評価のために,公共のデータセット,FairFaceとAffectNetを使用しました.

主要な成果:

  • 高い精度率を達成した:人種では81.1%,性別では95.8%,年齢層では80.0%,感情では59.4%.
  • ゼロショット評価でOpenAI GPT,Google Gemini,LLaVA,Google PaliGemmaと比較して優れたパフォーマンスを示しました.

結論:

  • 提案されているFaceScanPaliGemmaシステムは,顔の属性分類の精度に大きな進歩をもたらします.
  • 専門的なモデルを用いたマルチエージェントアプローチは,複雑な視覚言語のタスクに対して有望であることが示されています.